Spaces:
Running
Running
File size: 7,718 Bytes
39f384e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 |
import torch
from typing import Optional
from rvc.lib.algorithm.nsf import GeneratorNSF
from rvc.lib.algorithm.generators import Generator
from rvc.lib.algorithm.commons import slice_segments, rand_slice_segments
from rvc.lib.algorithm.residuals import ResidualCouplingBlock
from rvc.lib.algorithm.encoders import TextEncoder, PosteriorEncoder
class Synthesizer(torch.nn.Module):
"""
Base Synthesizer model.
Args:
spec_channels (int): Number of channels in the spectrogram.
segment_size (int): Size of the audio segment.
inter_channels (int): Number of channels in the intermediate layers.
hidden_channels (int): Number of channels in the hidden layers.
filter_channels (int): Number of channels in the filter layers.
n_heads (int): Number of attention heads.
n_layers (int): Number of layers in the encoder.
kernel_size (int): Size of the convolution kernel.
p_dropout (float): Dropout probability.
resblock (str): Type of residual block.
resblock_kernel_sizes (list): Kernel sizes for the residual blocks.
resblock_dilation_sizes (list): Dilation sizes for the residual blocks.
upsample_rates (list): Upsampling rates for the decoder.
upsample_initial_channel (int): Number of channels in the initial upsampling layer.
upsample_kernel_sizes (list): Kernel sizes for the upsampling layers.
spk_embed_dim (int): Dimension of the speaker embedding.
gin_channels (int): Number of channels in the global conditioning vector.
sr (int): Sampling rate of the audio.
use_f0 (bool): Whether to use F0 information.
text_enc_hidden_dim (int): Hidden dimension for the text encoder.
kwargs: Additional keyword arguments.
"""
def __init__(
self,
spec_channels: int,
segment_size: int,
inter_channels: int,
hidden_channels: int,
filter_channels: int,
n_heads: int,
n_layers: int,
kernel_size: int,
p_dropout: float,
resblock: int,
resblock_kernel_sizes: list,
resblock_dilation_sizes: list,
upsample_rates: list,
upsample_initial_channel: int,
upsample_kernel_sizes: list,
spk_embed_dim: int,
gin_channels: int,
sr: int,
use_f0: bool,
text_enc_hidden_dim: int = 768,
**kwargs,
):
super().__init__()
self.segment_size = segment_size
self.gin_channels = gin_channels
self.use_f0 = use_f0
self.enc_p = TextEncoder(
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
text_enc_hidden_dim,
f0=use_f0,
)
if use_f0:
self.dec = GeneratorNSF(
inter_channels,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=gin_channels,
sr=sr,
is_half=kwargs["is_half"],
)
else:
self.dec = Generator(
inter_channels,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=gin_channels,
)
self.enc_q = PosteriorEncoder(
spec_channels,
inter_channels,
hidden_channels,
5,
1,
16,
gin_channels=gin_channels,
)
self.flow = ResidualCouplingBlock(
inter_channels,
hidden_channels,
5,
1,
3,
gin_channels=gin_channels,
)
self.emb_g = torch.nn.Embedding(spk_embed_dim, gin_channels)
def _remove_weight_norm_from(self, module):
"""Utility to remove weight normalization from a module."""
for hook in module._forward_pre_hooks.values():
if getattr(hook, "__class__", None).__name__ == "WeightNorm":
torch.nn.utils.remove_weight_norm(module)
def remove_weight_norm(self):
"""Removes weight normalization from the model."""
for module in [self.dec, self.flow, self.enc_q]:
self._remove_weight_norm_from(module)
def __prepare_scriptable__(self):
self.remove_weight_norm()
return self
def forward(
self,
phone: torch.Tensor,
phone_lengths: torch.Tensor,
pitch: Optional[torch.Tensor] = None,
pitchf: Optional[torch.Tensor] = None,
y: Optional[torch.Tensor] = None,
y_lengths: Optional[torch.Tensor] = None,
ds: Optional[torch.Tensor] = None,
):
"""
Forward pass of the model.
Args:
phone (torch.Tensor): Phoneme sequence.
phone_lengths (torch.Tensor): Lengths of the phoneme sequences.
pitch (torch.Tensor, optional): Pitch sequence.
pitchf (torch.Tensor, optional): Fine-grained pitch sequence.
y (torch.Tensor, optional): Target spectrogram.
y_lengths (torch.Tensor, optional): Lengths of the target spectrograms.
ds (torch.Tensor, optional): Speaker embedding.
"""
g = self.emb_g(ds).unsqueeze(-1)
m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
if y is not None:
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
z_p = self.flow(z, y_mask, g=g)
z_slice, ids_slice = rand_slice_segments(z, y_lengths, self.segment_size)
if self.use_f0 and pitchf is not None:
pitchf = slice_segments(pitchf, ids_slice, self.segment_size, 2)
o = self.dec(z_slice, pitchf, g=g)
else:
o = self.dec(z_slice, g=g)
return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
return None, None, x_mask, None, (None, None, m_p, logs_p, None, None)
@torch.jit.export
def infer(
self,
phone: torch.Tensor,
phone_lengths: torch.Tensor,
pitch: Optional[torch.Tensor] = None,
nsff0: Optional[torch.Tensor] = None,
sid: torch.Tensor = None,
rate: Optional[torch.Tensor] = None,
):
"""
Inference of the model.
Args:
phone (torch.Tensor): Phoneme sequence.
phone_lengths (torch.Tensor): Lengths of the phoneme sequences.
pitch (torch.Tensor, optional): Pitch sequence.
nsff0 (torch.Tensor, optional): Fine-grained pitch sequence.
sid (torch.Tensor): Speaker embedding.
rate (torch.Tensor, optional): Rate for time-stretching.
"""
g = self.emb_g(sid).unsqueeze(-1)
m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
if rate is not None:
head = int(z_p.shape[2] * (1.0 - rate.item()))
z_p, x_mask = z_p[:, :, head:], x_mask[:, :, head:]
if self.use_f0 and nsff0 is not None:
nsff0 = nsff0[:, head:]
z = self.flow(z_p, x_mask, g=g, reverse=True)
o = (
self.dec(z * x_mask, nsff0, g=g)
if self.use_f0
else self.dec(z * x_mask, g=g)
)
return o, x_mask, (z, z_p, m_p, logs_p)
|